Fetal Distress Syndrome is an abnormal condition during gestation or at the time of delivery, marked by altered heart rate or rhythm and leading to compromised blood flow or changes in blood chemistry. Detection of fetal distress syndrome is done in obstetrics by Cardiotocography, the simultaneous measurement of fetal heart rate and uterine contractions. The change in fetal heart rate as a response to uterine contractions is the diagnostic basis of fetal distress syndrome. See, e.g., “Cardiotocography”, van Geijn, H. P., Textbook of Perinatal Medicine, Parthenon Publishing, 1998, Vol. 2, p. 1424-8. In every-day obstetrics practice, physicians routinely prescribe cardiotocograms to detect fetal distress syndrome.
Cardiotocography, or electronic fetal monitoring (EFM), is a common non-invasive diagnostic technique utilized in obstetrics to detect and determine the extent of Fetal Distress Syndrome. Cardiotocography uses the simultaneous measurement of the fetal heart rate (“cardio”) and the uterine contractions (“toco”) to detect any abnormalities.
Current technology is composed of a central unit, which contains a printer, a Doppler fetal monitor (to register the fetal heart rate), and a tocodynamometer (to register uterine contractions). In currently used equipment, the sensors are affixed to the abdomen of the mother and connected to the central unit via connecting cables.
Typically, a conventional tocodynamometer is a strain gauge attached to a belt around the abdomen of the patient. The strain gauge detects the tension on the uterus wall during contractions. Also conventionally, a Doppler ultrasound transducer measures fetal heart rate. The result is a graphical overlay of both measurements, seen either on a screen or on paper. By comparing changes in fetal heart rate to maternal contractions, the healthcare provider assesses the status of the fetus and determines if fetal distress is present.
Currently, obstetric patients requiring EFM are referred to either a hospital or outpatient clinic setting where monitoring takes place under the physical presence of a technician or nurse. While resting in bed, the sensors are placed on the patient and the sensors are connected to a measuring apparatus with cables, thus limiting the patient's mobility. The measuring apparatus displays two simultaneous graphs, one with the fetal heart rate and the other with the uterine contractions (on paper or screen). The practitioner determines the presence and the severity of Fetal Distress Syndrome based on these two graphs. See, e.g., “Interpretation of the Electronic Fetal Heart Rate During Labor”, American Academy of Family Physicians (1999).
Traditional fetal monitoring systems include are relatively bulky, expensive and intended to be used in designated centers (e.g., hospitals/physicians or offices). This arrangement raises several issues.
First, there exists a limited accessibility to fetal monitoring. Currently, in United States, pregnant mothers must commute to either a physician's office or a designated fetal monitoring center and these centers are often difficult for patients to access. This means that the pregnant mother should take a trip to the hospital for a monitoring session which puts the burden of time and expense both on the mother and accompanying person(s) as well as the healthcare system. Therefore, with traditional systems monitoring of pregnant mothers, who are not categorized as high risk, is limited to a few times during course of pregnancy. For example, typical testing is on the order of 2 times every week during the last trimester. This leads potentially to reduced efficacy of monitoring in terms of missing critical incidents. Immobility of the traditional system also means that pregnant mothers in remote areas and/or in the underserved areas with limited access to the healthcare system (e.g., in the case of many developing countries) are not being tested at all.
Second, there is limited mobility of the patient during fetal monitoring. Pregnant mothers who undergo fetal monitoring require a minimum of 45 minutes and up to 4 hours for each monitoring session. During this time the patient must remain in a relaxed position (usually recumbent) connected to the recording device. Putting on and adjusting the position of fetal monitoring system sensors takes substantial amount of time (i.e., on the order of 10-20 minutes). Using the traditional wired fetal monitoring system, in case that the patient needs to move during the test (e.g. goes to bathroom or the like) the setup needs to be removed and placed back afterwards. This adds additional time and cost burden in the hospitals.
Third, there is a lack of remote accessibility to data for evaluation. Currently most cardiotographic devices do not have the capability of digital storage and transfer. The usual manner in which a fetal monitoring study occurs involves a paper tracing that is carried to the health care provider or Physician for interpretation, and then stored in the patient's medical record. Often the length of these strips exceeds the capacity for storage for clinical, private physician practices and even hospital systems. Additionally, the lack of digital data transferability means that interpreting the data is possible in only places that trained care providers (i.e. nurses or physicians) are accessible.
Doppler ultrasound is a non-invasive monitoring approach to extract information about moving structures inside the body. It can be used for diagnosis of many cardiovascular conditions as well as in fetal health monitoring. Current ultrasonic technologies rely on bedside monitoring that is limited to the hospital and clinical settings. A major obstacle in transforming the traditional ultrasonic technologies into the emerging wireless health solutions is the significantly high computational complexity of the algorithms that process the plethora of the Doppler shifted data acquired from ultrasound transducers.
With the growing interest in wireless health technologies and their potential applications, efficient design and development of wearable medical devices is becoming unprecedentedly important to researchers in both academia and industry. See, e.g., R. Jafari, S. Ghiasi, and M. Sarrafzadeh, “Medical Embedded Systems,” in Embedded System Design: Topics, Techniques and Trends, ser. IFIP Advances in Information and Communication Technology, A. Rettberg, M. Zanella, R. Düner, A. Gerstlauer, and F. Rammig, Eds. Springer Boston, 2007, vol. 231, pp. 441-444. The main driving factors in designing this new generation of the health paradigm include cost, power consumption, and wearablility, with power consumption being the center of many research efforts due to its dramatic influence on other design objectives. See, e.g., C. Park, P. Chou, Y. Bai, R. Matthews, and A. Hibbs, “An Ultra-wearable, Wireless, Low Power ECG Monitoring System,” in Biomedical Circuits and Systems Conference, 2006. BioCAS 2006. IEEE, December 2006, pp. 241-244; P. Zappi, C. Lombriser, T. Stiefineier, E. Farella, D. Roggen, L. Benini, and G. Troster, “Activity Recognition From On-Body Sensors Accuracy-Power Trade-off By Dynamic Sensor Selection,” Lecture Notes in Computer Science, vol. 4913, p. 17, 2008; V. Leonov, P. Fiorini, S. Sedky, T. Torfs, and C. Van Hoof, “Thermoelectric Mems Generators as a Power Supply for a Body Area Network,” vol. 1, June 2005, pp. 291-294; S. Xiao, A. Dhamdhere, V. Sivaraman, and A. Burdett, “Transmission Power Control in Body Area Sensor Networks for Healthcare Monitoring,” IEEE Journal on Selected Areas in Communications, vol. 27, no. 1, pp. 37-48, 2009; and H. Ghasemzadeh and R. Jafari, “A Greedy Buffer Allocation Algorithm for Power-Aware Communication in Body Sensor Networks,” in Proceedings of the eighth IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, ser. CODES/ISSS '10. New York, N.Y., USA: ACM, 2010, pp. 195-204.
An important angle of low-power design is development of efficient signal processing and data reduction algorithms that reduce computation load of the processing units, allowing low-power low-cost processors to be embedded with the wearable device. While much work has been done on designing signal processing algorithms for a variety of sensing modalities such as motion sensors (H. Ghasemzadeh, V. Loseu, and R. Jafari, “Structural Action Recognition in Body Sensor Networks: Distributed Classification Based on String Matching,” IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 2, pp. 425-435, 2010; A. Barth, M. Hanson, H. Powell, and J. Lach, “Tempo 3.1: A Body Area Sensor Network Platform for Continuous Movement Assessment,” in Wearable and Implantable Body Sensor Networks, 2009. BSN 2009. Sixth International Workshop on, 2009, pp. 71-76.), Electrocardiography (D. Jun, X. Miao, Z. Hong-hai, and L. Wei-feng, “Wearable ECG Recognition and Monitor,” in Computer-Based Medical Systems, 2005. Proceedings. 18th IEEE Symposium on, June 2005, pp. 413-418; M. Ayat, K. Assaleh, and H. Al-Nashash, “Prototype of a Standalone Fetal ECG Monitor,” in Industrial Electronics Applications (ISIEA), 2010 IEEE Symposium on, 2010, pp. 617-622), and photo-plethysmogram sensors (J. Espina, T. Falck, J. Muehlsteff, and X. Aubert, “Wireless Body Sensor Network for Continuous Cuff-less Blood Pressure Monitoring,” in Medical Devices and Biosensors, 2006. 3rd IEEE/EMBS International Summer School on, 2006, pp. 11-15), ultrasonic signal processing for stringent constrained computing platforms has not been studied in the past.
Traditional ultrasound technologies have been used in a variety of application domains such as ultrasound imaging (E. J. Gussenhoven, C. E. Essed, C. T. Lancée, F. Mastik, P. Frietman, F. C. van Egmond, J. Reiber, H. Bosch, H. van Urk, J. Roelandt, and N. Bom, “Arterial Wall Characteristics Determined by Intravascular Ultrasound Imaging: An in vitro Study,” Journal of the American College of Cardiology, vol. 14, no. 4, pp. 947-952, 1989, ACC Anniversary Seminar) to produce pictures of the inside of the body, blood flow monitoring (A. Azhim, J. Yamaguchi, Y. Hirao, Y. Kinouchi, H. Yamaguchi, K. Yoshizaki, S. Ito, and M. Nomura, “Monitoring Carotid Blood Flow and ECG for Cardiovascular Disease in Elder Subjects,” in Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the, 2005, pp. 5495-5498) to measure velocity of blood flow in different arteries for use in monitoring cardiovascular diseases, and Cardiotocography (C.-Y. Chen, J.-C. Chen, C. Yu, and C.-W. Lin, “A Comparative Study of a New Cardiotocography Analysis Program,” in Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, September 2009, pp. 2567-2570) to measure fetal heart rate and assess the effect of uterine contractions on fetal heart rate. However, the main challenge in transition from traditional ultrasound technologies to wearable platforms is the demand for a very high computational power. Compared to the other sensing modalities, ultrasound signals require a relatively high sampling frequency, producing large volumes of data that need to be processed. For instance, in a blood flow monitoring application, relevant information may appear in the frequency band of 100-4200 Hz, which may require a sampling frequency of 10 kHz as used in Azhim, et al, above. Moreover, a minimum sampling rate of 1600 Hz for capturing fetal movements is suggested in C.-Y. Chen, J.-C. Chen, C. Yu, and C.-W. Lin, “A Comparative Study of a New Cardiotocography Analysis Program,” in Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, September 2009, pp. 2567-2570. The large volume of sampled ultrasonic signals needs to undergo fast signal conditioning algorithms in order to extract relevant information in real-time.
As to patents, Rapoport, U.S. Pat. No. 5,257,627, discloses a portable apparatus for the non-invasive, simultaneous, self-testing of fetal and maternal signals. It includes a user display to indicate that the device is operational, an ultrasonic system to detect fetal heart rate connected to said device, a detection system for maternal input signal connected to said device, wherein the device has signal processor for simultaneously processing fetal heart rate and maternal input signals, and also has a communication linking means for the simultaneous transmission of fetal heart rate and maternal input data to a remote output device.
Lewis et al., U.S. Pat. No. 6,115,624, discloses an intrauterine catheter device for monitoring fetal and/or maternal heart rate, including an elongate housing having proximal and distal portions, an array of ECG electrodes on the distal portion and one or more acoustic or other mechanical sensors on the distal portion. A pressure transducer may also be provided on the distal portion. Processor circuitry compares the ECG signal with the output signal of the acoustic sensor to derive fetal and/or maternal heart rate. An intrauterine catheter device is also provided, including a reference electrode on its distal portion, and an array of active electrodes spaced apart from one another on the distal portion. The device may also include a pressure transducer on the distal portion and processor circuitry coupled to the array of active electrodes and/or to the reference electrode for deriving fetal ECG from signals produced by the array of active electrodes. Alternatively, the array of electrodes and acoustic sensors may be provided on a flexible pad that may be secured to the abdomen of a pregnant mother. An intrauterine catheter device is also provided, including a plurality of lumens communicating with a differential pressure transducer provided on its distal portion, and having a zeroing switch on its proximal portion for resetting the pressure transducer in situ.
Powell et al., U.S. Patent Application No. 2006/0149597, makes the following statements in the patent. It is said to provide a data processing tool for the viewing of real-time, critical patient data on remote and/or mobile devices. It is said that the tool renders graphical data on the screen of the remote device in a manner that makes it practical for the health care provider to accurately and timely review the data for the purpose of making an informed decision about the condition of the patient. Charting control is established and implemented using the latest GDI+, GAPI and PDA drawing techniques. The charting components provide landscape support, an ability to overlay patient data and patient images, zoom in/zoom out, custom variable speed scrolling, split screen support, and formatting control. It is said that the methodology operates as an asynchronous application, without sacrificing processing time in the mobile/handheld device. The methodology allows the critical patient data to be streamed in real-time to the handheld device while conserving enough CPU power to simultaneously allow the end user to interact at will with the responsive display application. The methodology is structured using object oriented concepts and design patterns. Each logical tier of the methodology, from the data access objects and the charting control objects, to the user interface objects, is structured with precise interfaces. The methodology implements an IT management console that allows system managers to monitor the exchange of data between hospital systems and the primary database, including all patient data packets, notifications and alerts, connected remote devices.
Hayes-Gill et al., U.S. Pat. No. 7,532,923, it discloses apparatus for detecting the heart rate of a fetus. The apparatus includes at least two detectors for detecting heart beats of the fetus, each detector comprising at least two electrodes for detecting ECG signals. A processor, which is coupled to the detectors, is used to process the ECG signals received from each detector and determine the heart rate of the fetus.
James et al., U.S. Patent Application No. 2007/0213672 discloses a monitor for fetal behavior by receiving ECG data from a set of electrodes attached to a material body. A waveform pre-processor identifies a succession of fetal ECG complex waveforms within the received data and a waveform processor determines differences in the processor succession of fetal ECG complex waveforms over time. An event logger determines from the determined differences a number of fetal movements during the period of time. Fetal spatial presentation and/or position within the uterus may also be determined from fetal ECG data acquired from a plurality of electrodes positioned on the maternal abdomen in a predetermined configuration. A number of fetal ECG complex waveforms are identified within the data, and each of the waveforms is compared with a set of predetermined fetal ECG complex templates ascribed to the predetermined electrode configuration to determine a template that best matches the identified fetal ECG waveforms.
Hayes-Gill et al., WO 2001/004147, it discloses a system for detecting uterine activity uses cutaneous electrodes on the maternal abdomen to obtain electrophysiological signals that can be used to obtain fetal and maternal heart rate. The apparatus includes a first input for receiving electrical signals from the cutaneous electrodes and a second input for receiving movement signals indicative of a movement of the maternal body from a movement detector. A signal processor separates a uterine electromyogram signal from fetal and maternal heart rate signals and filters out motion artifacts from the electromyogram using the movement signals. An output presents electrohysterogram (EHG) data from the uterine electromyogram signal.
Against this background is a compelling need to both bring healthcare to the underserved population, as well as to deliver more effective and cost effective healthcare. Further, there is a need to provide a marriage of wireless technologies in a way that are both safe and effective. Despite these compelling needs, the difficulty in detecting Fetal Distress Syndrome remains.